Sleep Quality Mediating the Relationship between Workload Stress and Emotional Exhaustion in Employed Females
Bibliographic record
Abstract
This study aimed to examine the mediating role of sleep quality in the relationship between workload stress and emotional exhaustion among employed females. A descriptive correlational design was adopted, and data were collected from 390 employed women in Canada using standardized self-report instruments. Workload stress was measured by the Workload Subscale of the Occupational Stress Inventory–Revised (Osipow, 1998), sleep quality by the Pittsburgh Sleep Quality Index (Buysse et al., 1989), and emotional exhaustion by the Maslach Burnout Inventory (Maslach & Jackson, 1981). Sampling was determined using the Morgan and Krejcie (1970) table, ensuring representativeness across occupational groups. Data analysis was conducted using SPSS-27 for descriptive and correlational statistics and AMOS-21 for Structural Equation Modeling (SEM) to test the mediation model. Model fit was evaluated through multiple indices, including χ²/df, GFI, AGFI, CFI, TLI, and RMSEA. The results indicated that workload stress significantly predicted emotional exhaustion both directly (β = 0.41, p < .001) and indirectly through sleep quality (β = 0.16, p < .001). Workload stress was positively associated with poor sleep quality (β = 0.38, p < .001), and poor sleep quality was strongly related to higher emotional exhaustion (β = 0.44, p < .001). The total effect of workload stress on emotional exhaustion was substantial (β = 0.57, p < .001). Model fit indices demonstrated an excellent fit (χ²(54) = 87.43, χ²/df = 1.62, GFI = 0.96, AGFI = 0.93, CFI = 0.98, TLI = 0.97, RMSEA = 0.041), confirming the adequacy of the hypothesized model. The study highlights that workload stress significantly contributes to emotional exhaustion among employed females, with sleep quality serving as a crucial mediating mechanism. These findings emphasize the importance of sleep health as a psychological and physiological buffer in occupational stress management. Organizations should prioritize interventions that regulate workload and promote restorative sleep to mitigate emotional exhaustion and enhance employee well-being.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.043 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.006 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".